Evaluating ecological integrity and social equity in national parks : case studies from Canada and South Africa
Bibliographic record
Abstract
There are concerns that many national parks worldwide are ineffective at conserving biological diversity and ecosystem processes, are socially unjust in their relations with Indigenous communities, or both. This dissertation asks: can national parks protect ecological integrity and concurrently address social equity issues? It presents empirical results of a systematic evaluation of six case study national parks in Canada and South Africa. Purposive sampling was used to select the six case study national parks. Data sources included State of the Park Reports; park ecological monitoring data; archival data; and semi-structured interviews with park biologists, managers, and Indigenous members of park co-management boards. Status and trend assessments and effectiveness evaluations of park ecological monitoring data were used to evaluate how effectively the parks addressed three ecological integrity criteria. Results show that all six parks effectively addressed the priority indicators for which they had monitoring data. However, the effectiveness ratings of each park decreased when all indicators, including those identified as priorities but lacking monitoring data, were analysed. This indicates that the parks had generally identified more priority indicators than they were actually able to address (for reasons including lack of budget or trained staff, managerial challenges). Thematic coding of semi-structured interview and archival data, and the assignation of numerical ratings to these data, were used to evaluate how effectively the parks addressed three equity criteria. Results show that all but one of the case study parks were equitable, parks with more comprehensive co-management and support from neighbouring Indigenous groups were more equitable than parks with lower levels of co-management, the parks with settled land claims were not necessarily more equitable overall, and a few parks were found to be co-managed in name only. The overall results of this evaluation demonstrate that parks effective at protecting ecological integrity can also successfully address social equity, but that further efforts to integrate these two realms are both possible and necessary. A logical starting point would be to build upon those existing integrative processes already institutionalised in many parks and protected areas: the co-management and integrated conservation and development efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".